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I. INTRODUCTION

From the start of the “New Order” government in 1966 up until 1997, broad-based economic development in Indonesia led to an impressive four-fold increase in per capita income. As a result, poverty in the country fell dramatically. Between 1976 and 1996 the number of poor people in Indonesia declined from 54.2 to 22.5 million.

However, the Asian Financial Crisis in 1997 brought a sharp halt to this economic miracle. Between 1997 and 1998 annual GDP growth in Indonesia fell by 13 percent and the incidence of poverty increased by 7 percent. The events of 1997-98 were a painful reminder of just how vulnerable the Indonesian economy was to external shocks coming from abroad.

For this reason, the current recession in Europe raises deep concerns about the possible impact of these events on the Indonesian economy. However, to date there has not been any quantitative analysis of the possible impact of these events on economic growth and poverty in Indonesia. The purpose of this paper is, therefore, to provide a modeling-based assessment of the possible effects of a recession in Europe, Japan and China - EJC countries, for short -- on Indonesia.

The objective of this paper is to use general equilibrium models to quantitatively assess the impact of the current recession in Europe, Japan and China on Indonesian poverty. It is expected that the results from such a general equilibrium analysis will give us important insights on how to design appropriate policy instruments for protecting the Indonesian economy from external disturbances.

The next section of this paper outlines the theory and the structure of the model. Section three outlines the data and the methodology, including the scenario of simulation. Section four provides result and analysis, while section five gives conclusion and the policy implications.

II.THEORETICAL FRAMEWORK AND EMPIRICAL LITERATURE 2.1. How external shock can affect poverty

It should be clear that what it means by external shock is in the context of exogenous change from the country’s persepective. In this case, those shocks can be a recession in a particular country, a group of countries, or one or some regions. The shocks can also be international price of commodities determined in the world market exogenous to the perspective a a particular country or the assumption of Indonesia being a small open economy.

Poverty should also be defined clearly. There are many dimension of poverty, but in this particular study, the focus is on expenditure poverty which is quantitatively measured as head-count poverty incidence, which can be written as Foster, Greer, Thorbecke (FGT) Index of poverty initially developed by Foster et al (1984):

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Where P is the poverty indicator, N is the number of population, H is the number of people whose expenditure per person is below theα≥poverty line, y is a vector of expenditureα per person in increasing order, z is the poverty line, 0 is a sensitivity parameter. When =1 this become the head-count poverty incidence, which is a proportion of population living below poverty line. This indicator is what will be used throughout this paper.

Figure 1 below illustrate how external shock, like global economic crisis of a particular countries or regions can affect poverty incidence in a national economy.

Figure 1.

The effect of external sock on poverty incidence

The impact of an EJC recession on the Indonesian economy can work through various channels, including trade, foreign direct and portfolio investment, and other monetary or financial channels. In this analysis, the focus is on the impact through the trade channel. A recession in the EJC countries will reduce imports and exports from countries like Indonesia. The impact on the Indonesian economy is likely to be negative, and the magnitude of the impact depends largely on the share that these countries have in Indonesian exports. This is the direct

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trade effect. The indirect trade effect is rather unclear. The reduction of imports from Europe, China and Japan will tend to reduce the world prices of commodities. Global deflation like this may reduce Indonesian exports yet increase Indonesian imports.

On the other hand, the decline in the export of goods from the EJC countries may increase the world prices of commodities. To some countries (including Indonesia), this might represent an opportunity to increase exports and to raise world market share. This effect tends to work positively for the Indonesian economy. To complicate things, these effects work differently among different countries, each with a different type of repercussion on the Indonesian economy. Therefore, the overall net impact on the Indonesian economy is difficult to predict and calls for a computable general equilibrium analysis.

The effect on poverty depend on the general equilibrium outcome of prices or new equilibrium prices changed due to the external shocks. There are two kind of prices that at the end will affect through different channels to households. First of all is the price of factors of production will affect household income. The impact on households will be different even though all households face the same price of capital or labor because poor and non-poor households has different composition of household income. Rich households has more capital income than labor, while poor households has more labor income.

Another channels is its impact on price of commodities. Again, although all households face the same price of commodities, the composition of household ecpenditures may differ. Poor household may buy less of travels but more of rice, while rich housholds may buy more of gasoline and other luxurious items.

The final impact on these two channels are on the real expenditure, or expenditure per capita. Poverty incidence then is used based on this real expenditure per person before and after the shocks and the difference is calculated as the impact of external shocks on poverty incidence.

2.2. Empirical literature

There have been some studies looking at the of global crisis, particuarly the 2008-2009 crisis, with particular attention on the impact on poverty. In this review, only studies that uses economy-wide model, microsimulation model, or combination of both are reviewed.

Wong, (2012) quantifies the macroeconomic and poverty impacts of the 2008-2009 world economic crisis on Ecuador, including the effects of the main policy responses of the Ecuadorian Government to face the crisis. The main hypothesis highlights the magnitude of two transmission channels: trade and remittances. The study applies a single-country static computable general equilibrium model for Ecuador combined with a microsimulation model. From a distributional point of view, the impacts of the crisis were progressive, affecting more negatively households in the highest income quintile. A key channel of transmission is the fall

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in capital returns and wages of skilled labor. These factors are used intensively in the oil sector, a key sector of the Ecuadorian economy, and one of the hardest hit by the global crisis. There are differentiated poverty impacts: poverty may increase if labor is assumed sector specific, and it may be reduced if labor were mobile.

(Habib, Narayan, Olivieri, & Sanchez-Paramo, 2010b) develops a simple micro-simulation method, modifying models from existing economic literature, to measure the poverty and distributional impact of macroeconomic shocks by linking macro projections with pre-crisis household data. The approach is then applied to Bangladesh to assess the potential impact of the slowdown on poverty and income distribution across different groups and regions. The poverty headcount rate (based on the upper poverty line) and extreme poverty rate (based on the lower poverty line) are expected to be 0.4 and 0.2 percentage points higher in 2009, respectively, as a result of the crisis.

Using a microsimulation analysis, (Ajwad, Francisco, & Azam, 2012) look at the distributional impacts of the 2009 financial crisis on households in Latvia. The simulations show that Latvia experienced a sharp rise in poverty, widening of the poverty gap, and a rise in income inequality due to the economic contraction in 2009. The 18 percent contraction in gross domestic product (affecting mainly trade hotels and restaurants, construction, and manufacturing) likely led the poverty head count to increase from 14.4 percent in 2008 to 20.2 percent in 2009.

(Ahmed & Donoghue, 2010) adopt a macro-micro framework in order to evaluate the impact of a global crisis on the Pakistan economy. They use a ‘top-down’ approach to combine a static computable general equilibrium model with a microsimulation model. Our results suggest that between 2007 and 2009 the poverty headcount ratio is likely to have increased by almost 80 percent, from 22 to 40 percentage points. However, their results also show that this increase is attributable in part to the fuel and food crisis that preceded the financial crisis. They also indicate a differential impact, with wage increases for farm workers and a decrease in wages for skilled labour.

(Weeks, 2009) using a macroeconomic model finds that the global financial crisis will cause a fall in export earnings in Sierra Leone of approximately fifteen percent in 2009 compared to 2008. He find that this decline in exports earnings could result in a fall in national income of almost ten percent and based on the income distribution in the 2003 household survey, a ten percent decline in national income would increase poverty by twelve percent of th population, or about 600,000 people.

(Habib, Narayan, Olivieri, & Sanchez-Paramo, 2010a) uses a micro-simulation approach to assess the poverty and distributional effects of the crisis in the Philippines. They find increases in both the level and the depth of aggregate poverty. Income shocks are relatively large in the middle part of the income distribution. They also find that characteristics of people who become poor because of the crisis are different from those of both chronically poor people and the general population.

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(Bibi et al., 2010) evaluate the potential impacts of the 2008/09 global economic crisis on child poverty in Cameroon. A dynamic computable general equilibrium (CGE) model is used to simulate various scenarios of the economic crisis together with policies which respond to the crisis. The study shows that the crisis is projected to lower the real GDP growth rate by 1.3 percentage points in 2009, 0.9 in 2010 and 0.8 in 2011. The crisis would also bring about a 1.05% increase in the number of children who were poor in monetary terms in 2008 and a 4% increase in 2009, 2010 and 2011, compared to the situation without a crisis.

In their analysis, (Estrades & Llambi, 2013) evaluate the impact of the 2008 global economic crisis on Uruguayan economy through two main channels: collapse in global trade and drop in capital flows. They apply a computable general equilibrium model linked to microsimulations to analyze the distributional impacts of these policies and assess their effectiveness. They find that an increase in public investment was the only policy effective in mitigating the negative impact of the crisis on extreme poverty.

In a study which focus on conceptual framework with the application to Vietnam, (Coxhead, Linh, & Tam, 2012) present a framework for understanding the direct and indirect welfare effects of a global market shock. They quantify transmission of the shock from global indicator prices to domestic markets. Then they use an applied general equilibrium model to simulate the economic effects of the price changes. A recursive mapping to a nationally representative household living standards survey permits them to identify in detail the ceteris paribus effects of the shock on household incomes and welfare. In this analysis, interregional and intersectoral labor market adjustments emerge as key channels transmitting the effects of global price shocks across sectors and among households.

(Corong & Taningco, 2011) analyzes how the global crisis may have affected the Philippine economy. To the extent that the Philippines is more globally integrated through trade and labor flow channels than the financial sector, it is expected that impact of the global crisis will weigh heavily on the “real” side of the economy. A dynamic computable general equilibriu m (CGE) model linked to a micro-simulation module used trace effects: from the macro-economic to the microeconomic level; from output and factor supplies and demands to commodity and factor prices; and from household incomes to levels of poverty and income distribution. Simulation results suggest that all households experience a significant reduction in real income. Both inequality and poverty increase, with urban dwellers experiencing a higher in crease in poverty relative to their rural counterparts as most export-oriented industries are located in the urban areas and returns to factors intensively used by these industries fall.

To summarize, most of the studies above suggests that global financial crisis affect the poor negatively. However, the magnitude is different; there are exceptions where the impact on poverty is negligible because the crisis affect mostly the rich; and policy responses can have significant impact on the final effect of the crisis. There is however, a methodological gap in most of the studies in how they linked global shocks into the national model. The analysis could

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individual inputs demanded by the firm. Tthe primary factors of production are: land, labor, and capital. Firms also purchase intermediate

•inputs, some of which are produced domestically, and some of which are imported. For imports, the intermediate inputs must be sourced from particular exporters. The strong nature of this kind of multi-country model.

•Regional household (a representative of household for each country) behavior is governed by an aggregate utility function, specified over composite private consumption, composite government purchases, and savings. In the GTAP model we employ a special case of the Stone-Geary utility function, whereby all subsistence shares are equal to zero.

•Government demands follows the Cobb-Douglas utility function.

•Factors are imperfectly mobile in a GTAP model. The mobility of these endowments is described with a constant elasticity of transformation (CET) revenue function.

Database

In this paper the GTAP database version 7 is used. (Hertel, 1997)describe the database as having 113 regions. The database was constructed from input-output table from each of the 113 countries and combined with a well-developed inter-regional trade flows data. Detailed description of the database including its construction can be found in (Narayanan, Badrie, & Walmsley, 2008)

3.2.The structure of INDONESIA-E3 model Theoretical structure

Since the GTAP model cannot analyze distribution results within countries, we use the INDONESIA-E3 model to simulate the effect of an EJC recession on poverty in Indonesia. The strength of this multi-sector, multi-household CGE model of the economy is distributional analysis. Most of its structural features are standard, but its capacity for disaggregation of household structure facilitates analysis of how exogenous shocks affect poverty and inequality.

The theoretical structure of INDONESIA-E3 model is conventional for static general equilibrium models. In particular, the equations in INDONESIA-E3 model represent the following economic behaviour:

•Production sectors minimizing cost of production given a Constant Elasticity of Substitution technology. A system of factor demand equation is derived and specified in the model. This relates the demand for each primary factor to industry outputs and prices of each of the primary factors (labor, capital, land, and intermediate inputs). This reflects the assumption

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that factors of production may be substituted for one another in ways that depend on factor prices and on the elasticities of substitution between the factors.

•Users of commodities which include industries, households, investors, government sectors form a system of demand equations. This demand system for each of these users consists of three layers (nested demand system). Consumers/users choose the optimal combination of domestically-produced and imported commodities. The last layer is they choose the optimal combination of different commodities responding the prices and budget constraints that they face. For household, a Linear Expenditure Demand System (LES) is specified.

•The household supplies of skilled and unskilled labor as well as capital and land.

•A distinction between four kinds of labor: agricultural labor, manual/production worker, clerical workers, and managerial workers. These are are ‘nested’ within the industry production functions. In each industry, all kind of labor enter a CES production function to produce ‘labor’, which itself enters a further CES production function for industry output.

•A set of export demand functions, indicating the elasticities of foreign demand for Indonesia’s exports to the rest of the world.

•Rates of import tariffs and excise taxes across commodities, rates of business taxes, value added taxes and corporate income taxes across industries, and rates of personal income taxes across household types which reflect the structure of the Indonesian tax system

•A set of macroeconomic identities which ensures that standard macroeconomic accounting conventions are observed.

In general, the demand and supply equations for private-sector agents are derived from the solutions to these agents microeconomic optimization problems (cost minimization for firms and utility maximization for households). The agents are assumed to be price-takers, with producers operating in competitive markets with zero profit conditions, reflecting the assumption of constant returns to scale.

The unique feature of INDONESIA-E3 model which is very relevant in this study is the disaggregation of household by expenditure classes which allows for precise estimates of the distributional impact and poverty incidence. In the literature of the poverty impact analysis using CGE models, this class of model is called an integrated CGE model (Bourguignon, Robilliard, & Robinson, 2003). This class of model normally has disaggregated households which link each of the households to both sources of income (through market of factors of production) and expenditure (through market for commodities). This should be distinguished from other class of model which is called top-down, where the CGE model is separate from the poverty module, and between them is only one directional relationship. In the integrated model, there is no separation between CGE model and poverty module because all are in one model.

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Table 1 shows that the countries most negatively affected by a European recession are those in the Middle East and North African (MENA) region. With a 2 percent fall in the EU’s GDP, MENA’s GDP falls by almost 0.6 percent, an elasticity of 0.3. Europe is the biggest importer of this region’s energy and MENA countries are heavily dependent on energy exports.

In Asia, Singapore is the country most affected by a European recession because of its dependence on international trade. Japan and China, however, experience a slightly positive increase in GDP.

If China and Japan experience a recession of similar magnitude the impact on Indonesia’s GDP will not be stronger than the impact of the European crisis. But the impact is disproportionate because Japan and China are single countries while the EU consists of 27. A 2 percent decline in China’s GDP causes a 0.07 percent decline in Indonesia’s GDP and a similar decline in Japan’s GDP causes a 0.03 percent decline in Indonesia’s GDP. In other words, China’s potential impact on Indonesia is almost twice that of Japan’s.

The impact of recession in Japan will be felt most in Singapore, where GDP will fall 0.24 percent, and the impact of recession in China will be felt most in Thailand, where GDP will fall

0.33percent relative to baseline. Singapore and Thailand are among the most open economies in Asia.

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In sum, a European recession of a sensible magnitude (2 percent GDP deviation relative to business as usual) working through full trade linkage (taking into account indirect trade effects through other countries as well) will not have a very large negative impact on Indonesia’s GDP. The reason for this is that Indonesia’s economy depends little on external trade. This does not mean that European recession will not affect the world economy to a considerable extent; other countries, like Singapore and MENA countries, will be greatly affected.

With respect to poverty in Indonesia, the impact of a recession in Europe, Japan, and China is also small. A combined 2 percent decline in GDP in Europe, Japan, and China will increase Indonesia’s national poverty head count by only 0.19 percent (Table 2). The biggest impact will be if the recession is in either China, or all of the EU. The impact on a recession on poverty in Indonesia is small because (1) the impact on overall mean real consumption is small and (2) the impact is most strongly felt among higher income households.

As suggested in Figure 1, where percentage change in real expenditure is plotted on the percentile of expenditure per capita, the impact of recession in Europe, Japan, or China is similar. The impact increases with income status. For example, the effect of a recession in the Euro zone on real consumption of the richest 1 percent of urban households is 3 times bigger than on the poorest 1 percent. Similarly, the richest 1 percent of rural households experiences a decline in real consumption 4 times bigger than the poorest 1 percent. The result is similar in all simulations (Euro zone, EU, China, or Japan recession). The range of the ratio of the impact is between 2.3 to 2.9 for urban households, and between 3.3 to 4.4 for rural households. This suggests that the simulations have an inequality-reducing tendency.

The distributive effect discussed earlier can be explained by how recession affects (1) household income, particularly return on factors of production; and (2) the pattern of change in the household-specific consumption price index (CPI). The final effect on household real consumption is a function of those two factors.

Global recession tends to decrease the global prices of commodities. Transmitted to the Indonesian economy, this deflationary effect will tend to reduce commodity prices for domestic consumers. A careful look at how this affects households of different income status suggests that the effect is only slightly progressive if not neutral. Across all simulations, the ratio of the impact on household-specific CPI between the richest 1 percent and the poorest 1 percent is less than 1. This suggests that the progressivity of impact arises largely from the income side. Richer households tend to experience income falls far greater than poorer households.

Figure 2, which shows the simulated impact of recession in Europe, Japan and China on real income of different factors of production, explains this. Almost all factors of production experience real decline in income; however, the biggest decline is where ownership is concentrated among higher income households. Capital income declines more than labor income, and skilled labor income declines more than unskilled labor income. This pattern of impact favors poorer households whose incomes largely depend on unskilled labor.

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